Physical reservoir computing with graphene-based solid electric double layer transistor and the information processing capacity analysis

Abstract Physical reservoir computing (PRC) is helpful for power reduction in machine learning technology, although the challenge is to improve computational performance. In this study, we developed a PRC device utilizing ion-electron coupled dynamics in an electric double layer transistor (EDLT) consisting of monolayer graphene channels and a Li + conducting inorganic oxide thin film. The ambipolar transfer characteristics of graphene channels in the EDLT obtained complex and diverse drain current responses, providing high information processing capacity and high PRC performance in the nonlinear autoregressive moving average (NARMA) task.